Advanced Data Science Techniques with Python : Outputs

 1. Feature Engineering for Enhanced Predictive Power

Output:
The code uses Featuretools to generate features from the customer data provided in a CSV file. It creates an entity set, defines an entity from the DataFrame, and then generates features using deep feature synthesis. Finally, it prints the engineered features.
The output will be the first few rows of the engineered features DataFrame. It will display the engineered features for each customer.
The output will look something like this:
             zip_code  COUNT(transactions)  ...  SUM(transactions.amount)       MEAN(transactions.amount)
customer_id                                  ...                                                
1                60601                    3  ...                     267.09                  89.030000
2                90033                    3  ...                     221.89                  73.963333
3                10011                    4  ...                     278.74                  69.685000
4                60614                    5  ...                     303.64                  60.728000
5                60657                    3  ...                     210.47                  70.156667

[5 rows x 4 columns]
2. Time Series Analysis and Forecasting:
Output:
This Code performs seasonal decomposition on sales data, fits a Prophet model, generates future dates for forecasting, and prints the forecasted values. Let's break down the expected output for each part:
  1. Seasonal Decomposition Output:
    • result.trend: Trend component of the decomposition.
    • result.seasonal: Seasonal component of the decomposition.
    • result.resid: Residual component of the decomposition.
  2. Prophet Forecast Output:
    • The last few rows of the predictions DataFrame, which include the forecasted values for the future dates.
The output will look something like this:
0            NaN
1            NaN
2            NaN
3            NaN
4            NaN
           ...   
95    297.314498
96    312.454498
97    295.896183
98    276.269183
99    283.073106
Name: trend, Length: 100, dtype: float64

0     58.527108
1     26.004999
2     19.608065
3     -4.752871
4    -33.388302
        ...    
95    58.527108
96    26.004999
97    19.608065
98    -4.752871
99   -33.388302
Name: seasonal, Length: 100, dtype: float64

0     NaN
1     NaN
2     NaN
3     NaN
4     NaN
       ..
95    NaN
96    NaN
97    NaN
98    NaN
99    NaN
Name: resid, Length: 100, dtype: float64

             ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \
100  2024-05-10  276.269183  230.134852  323.975364   276.269183   276.269183   
101  2024-05-11  283.073106  234.243850  329.014174   283.073106   283.073106   
102  2024-05-12  283.875939  232.642435  329.003036   283.875939   283.875939   
103  2024-05-13  285.505319  238.730895  333.845565   285.505319   285.505319   
104  2024-05-14  287.134698  238.646308  336.046658   287.134698   287.134698   

     additive_terms  additive_terms_lower  additive_terms_upper     weekly  \
100      -12.166685            -12.166685            -12.166685 -12.166685   
101       -8.298307             -8.298307             -8.298307  -8.298307   
102       -9.956161             -9.956161             -9.956161  -9.956161   
103       -7.917464             -7.917464             -7.917464  -7.917464   
104       -6.256769             -6.256769             -6.256769  -6.256769   

     weekly_lower  weekly_upper  multiplicative_terms  \
100    -12.166685    -12.166685                   0.0   
101     -8.298307     -8.298307                   0.0   
102     -9.956161     -9.956161                   0.0   
103     -7.917464     -7.917464                   0.0   
104     -6.256769     -6.256769                   0.0   

     multiplicative_terms_lower  multiplicative_terms_upper        yhat  
100                         0.0                         0.0  264.102498  
101                         0.0                         0.0  274.774799  
102                         0.0                         0.0  273.919778  
103                         0.0                         0.0  277.587855  
104                         0.0                         0.0  280.877929  


  • trend, seasonal, and resid represent the trend, seasonal, and residual components respectively.
  • The last few rows of the predictions DataFrame show the forecasted values for the future dates along with other related information.
3. Natural Language Processing for Text Analytics:
Output: 
This code first performs sentiment analysis on customer reviews using NLTK's SentimentIntensityAnalyzer, then it preprocesses the text data using TF-IDF vectorization. Here's what the output will look like:
1. Sentiment Scores:
The sentiment scores for each review, calculated using the compound score from the SentimentIntensityAnalyzer.
2. TF-IDF Matrix:
The TF-IDF matrix representing the preprocessed text data.
0    0.6588
1   -0.5423
2    0.3400
3   -0.2732
4    0.8225
Name: sentiment_scores, dtype: float64

[[0.         0.40993715 0.         ... 0.         0.         0.        ]
 [0.         0.         0.51785612 ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]
 [0.         0.         0.         ... 0.         0.         0.        ]]

  • The first print statement shows the sentiment scores for each review.
  • The second print statement shows the TF-IDF matrix. Each row represents a review, and each column represents a word in the vocabulary. Values in the matrix represent the TF-IDF scores for each word in the corresponding review.

Comments

Post a Comment

Popular posts from this blog

How to use the statsmodels library in Python to calculate Exponential Smoothing

Image
Exponential smoothing is a widely used smoothening technique in business analytics that assigns exponentially decreasing weights to past observations. It is particularly useful for forecasting future values based on historical data. There are three main types of exponential smoothing methods:  simple exponential smoothing, double exponential smoothing, and triple exponential smoothing  (also known as Holt-Winters method). In pandas, you can utilize the  statsmodels  library in Python for exponential smoothing calculations. Here's an example of how to perform exponential smoothing using  statsmodels : The Code: # Import the required libraries import pandas as pd import statsmodels.api as sm # Create a DataFrame with a time series data data = {'Month': ['Jan', 'Feb', 'Mar', 'Apr'], 'Sales': [100, 120, 110, 130]} df = pd.DataFrame(data) # Set the 'Month' column as the index df.set_index('Month', inplace=True)
Read more

K-means Clustering 3D Plot Swiss roll Dataset

Image
  K-means is a widely used clustering algorithm in machine learning and data mining. It is an unsupervised learning algorithm that aims to partition a given dataset into distinct groups or clusters based on similarity of data points. The algorithm is called “K-means” because it divides the data into K clusters, where K is a user-specified parameter. K-means aims to minimize the within-cluster sum of squares. The objective is to have the data points within each cluster as similar as possible, while keeping the clusters as distinct as possible. However, it’s important to note that K-means is sensitive to the initial random selection of cluster centers. Therefore, it’s often recommended to run the algorithm multiple times with different initializations and choose the clustering with the lowest inertia. Here’s an example of K-means clustering using Python and machine learning: import numpy as np import matplotlib. pyplot as plt from sklearn. cluster import KMeans from sklearn. dat
Read more

How to detect Credit Card Fraud Using Python Pandas

Image
  Detecting fraud in credit card transactions is an important application of Machine Learning. Given below is a step-by-step guide on how to approach fraud detection using Python (Pandas and Scikit-Learn) with the Credit Card Fraud Detection Dataset from Kaggle: Data source: Credit Card Fraud Detection Dataset  https://www.kaggle.com/mlg-ulb/creditcardfraud Step 1: Data Preprocessing Start by importing the necessary libraries and loading the dataset into a Pandas DataFrame. import pandas as pd # Load the dataset data = pd.read_csv('creditcard.csv') #replace with the downloaded file path # Explore the dataset print(data.head()) Output: Step 2: Data Exploration Understand the dataset by checking its structure, summary statistics, and class distribution (fraudulent vs. non-fraudulent transactions). # Check the dataset shape print (data.shape) # Check summary statistics #print(data.describe()) # Check class distribution print (data[ 'Class' ].value_counts()) Output: ( 284
Read more